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Homomorphic Filtering of Speckle Noise From Computerized Tomography (CT) Images Using Adaptive Centre-Pixel-Weighed Exponential Filter
Martin Chinweokwu Eze, Ogechukwu N. Iloanusi, Uche A. Nnolim, Charles C. Osuagwu
Pages - 455 - 467     |    Revised - 01-12-2014     |    Published - 31-12-2013
Volume - 8   Issue - 6    |    Publication Date - November / December 2014  Table of Contents
Adaptive Filter, Exponential Filter, Speckle Noise, Homomorphic Filtering, CT Image, Centre-pixel, Centre-pixel-weighed.
Adaptive filters are needed to accurately remove noise from noisy images when the variance of noise present varies. Linear filter such as Exponential filter becomes effective in removing speckle noise when homomorphic filtering technique is used. In this paper, an Adaptive Centre- Pixel-Weighed Exponential Filter for removing speckle noise from CT images was developed. The new filter is based on varying the centre-pixel of the filter kernel based on the estimated speckle noise variance present in a noisy CT image. Ten samples of 85x73 CT images corrupted by speckle noise level ranging from 10% to 30% were considered and the new technique gave a reasonably accurate speckle noise filtering performance with an average Peak Signal to Noise Ratio (PSNR) of 70.2839dB compared to 69.0658dB for Wiener filter and 64.3711dB for the Binomial filter. The simulation software used in the paper is Matrix Laboratory (Matlab).
CITED BY (1)  
1 Eze, M. C., Iloanusi, O. I. N., & Osuagwu, C. O. C. (2015). mean of median absolute derivation technique mean of median absolute derivation technique for speckle noise variance estimation noise variance estimation in computerised tomography computerised tomography computerised tomography images. nigerian journal of technology (nijotech), 34(2), 368-374.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
A. B. Sankar and D. Kumar, “Performance Study of Various Adaptive Filter Algorithms for Noise Cancellation in Respiratory Signals Therefore , they can detect shape variations in the ensemble and thus they can obtain a better signal estimation . This paper focuses on ( i ) Model Resp,” Sgnal Processing:An International Journal, vol. 4, no. 5, pp. 267–278.
A. Cameron, D. Lui, A. Boroomand, J. Glaister, A. Wong, and K. Bizheva, “Stochastic speckle noise compensation in optical coherence tomography using non-stationary spline-based speckle noise modelling.,” Biomedical optics express, vol. 4, no. 9, pp. 1769–85, Jan. 2013.
A. K. E. Isaac, “T he Performance of Filters for Reduction of Speckle Noise in SAR Images of the Coast of the Gulf of Guinea,” International Journal of Information Technology, Modeling and Computing, vol. 1, no. 4, pp. 43–52, 2013.
A. Manseur, D. Berkani, and A. Mekhmoukh, “Adaptive filtering using Higher Order Statistics (HOS),” International Journal of Computer Science Issues, vol. 9, no. 2, p. 477, 2012.
A. Ozcan, A. Bilenca, A. E. Desjardins, B. E. Bouma, and J. Tearney, “Speckle Reduction in Optical Coherence Tomography Images using Digital Filters,” Journal of the Optical Society of America., vol. 24, no. 7, pp. 1901–1910, 2009.
A. Rafiee, M. H. Moradi, and M. R. Farzaneh, “Novel genetic-neuro-fuzzy filter for speckle reduction from sonography images.,” Journal of digital imaging, vol. 17, no. 4, pp. 292–300, Dec. 2004.
A. Singhal and M. Singh, “Speckle Noise Removal and Edge Detection Using Mathematical Morphology,” International Journal of Soft Computing and Engineering, vol. 1, no. 5, pp. 146– 149, 2011.
A. Stella and B. Trivedi, “Implementation of Order Statistic Filters on Digital Image and OCT Image?: A Comparative Study,” International Journal of Modern Engineering Research, vol. 2, no. 5, pp. 3143–3145, 2012.
B. S. Kramer, C. D. Berg, D. R. Aberle, and P. C. Prorok, “Lung cancer screening with low- dose helical CT: results from the National Lung Screening Trial (NLST).,” Journal of medical screening, vol. 18, no. 3, p. 110, Jan. 2011.
G. Ilango, “- Neighbourhood Median Filters to Remove Speckle Noise from CT – Images,” International Journals of Applied Information Systems, vol. 4, no. 10, pp. 40–46, 2012.
G. Padmavathi, P. Subashini, M. M. Kumar, and S. K. Thakur, “Comparison of Filters used for Underwater Image Pre-Processing,” International Journal of Computer Science and Network Security, vol. 10, no. 1, p. 2, 2010.
G. S. S, U. S. Nagar, and A. Safir, “REMOVAL OF SPECKLE NOISE FROM EYE IMAGES,” International Journal of Advanced Engineering Technology, vol. 2, no. 1, p. 3, 2011.
K. Balakrishnan, K. Sunil, A. V Sreedhanya, and K. P. Soman, “Effect Of Pre-Processing On Historical Sanskrit Text Documents,” International Journal of Engineering Research and Applications, vol. 2, no. August, pp. 1529–1534, 2012.
K. Karthikeyan, “Speckle Noise Reduction of Medical Ultrasound Images using Bayesshrink Wavelet Threshold,” vol. 22, no. 9, pp. 8–14, 2011.
K. Kutty and S. Ojha, “A Generic Transfer Function based Technique for Estimating Noise from Images,” International Journal of Computer Applications, vol. 51, no. 10, pp. 26–32, 2012.
L. M. Davala, “Directional Linear Minimum Mean Square- Error Estimation in Color Demosaicking,” vol. 2, no. 4, pp. 171–183, 2012.
M. C. Eze and C. C. Osuagwu, “Evaluation of the Performances of Homomorphic and Non- homomorphic Speckle Noise Filtering Techniques,” International Journal of Emerging Technologies in Computational and Applied Sciences ( IJETCAS ), vol. 10, no. 1, pp. 50–55, 2014.
M. Juneja and R. Mohana, “An Improved Adaptive Median Filtering Method for Impulse Noise Detection,” International Journal of Recent Trends in Engineering, vol. 1, no. 1, p. 274, 2009.
S. Abramov, V. Abramova, V. Lukin, N. Ponomarenko, B. Vozel, K. Chehdi, K. Egiazarian, and J. Astola, “Methods for Blind Estimation of Speckle Variance in SAR Images?: Simulation Results and Verification for Real-Life Data,” INTECH Computational and numerical Simulations, 2010.
S. Abramov, V. Zabrodina, V. Lukin, B. Vozel, K. Chehdi, and J. Astola, “Methods for Blind Estimation of the Variance of Mixed Noise and Their Performance Analysis,” INTECH Numeric Analysis, 2011.
S. Mann, “Comparametric Equations with Practical Applications in Quantigraphic Image Processing,” IEEE transactions on image processing, vol. 9, no. 8, pp. 1389–1406, 2000.
S. Solbø and T. Eltoft, “?-WMAP: a statistical speckle filter operating in the wavelet domain,” International Journal of Remote Sensing, vol. 25, no. 5, pp. 1019–1036, Mar. 2004.
V. Anand, S. Shah, and S. Kumar, “Intelligent Adaptive Filtering For Noise Cancellation,” International Journal of Advanced Research in Electrical,Electronics and Instrumentation Engineering, vol. 2, no. 5, pp. 2029–2039, 2013.
Mr. Martin Chinweokwu Eze
Department of Electronic Engineering Faculty of Engineering University of Nigeria Nsukka, 410001 - Nigeria
Mr. Ogechukwu N. Iloanusi
Department of Electronic Engineering Faculty of Engineering University of Nigeria Nsukka, 410001, Nigeria - Nigeria
Mr. Uche A. Nnolim
Department of Electronic Engineering Faculty of Engineering University of Nigeria Nsukka, 410001, Nigeria - Nigeria
Mr. Charles C. Osuagwu
Department of Electronic Engineering Faculty of Engineering University of Nigeria Nsukka, 410001, Nigeria - Nigeria